Ab Testing
If you like applying Ab Testing, every challenge here gives you a chance to practice it on a real industry brief.
- CodeAdvancedNew
Build a Hybrid Recommender for a Niche Consumer-AI Music App
You receive listening events (around 240 million plays) plus a content embedding per track (audio + curator tags). Build a collaborative filtering model (ALS or implicit-feedbac…
- Recommender Systems
- Collaborative Filtering
- Content Based Filtering
Data Mining and Knowledge Discovery - AnalysisAdvancedNew
Diagnose Modern Transport-Protocol Performance for an OTT Streamer
Receive the current delivery architecture (HTTP/2 origin + CDN), 4 weeks of Conviva-style QoE (quality of experience) metrics, and access to a synthetic-client harness (Linux + …
- Quic Http3
- Network Measurement
- Transport Protocols
Advanced Computer Networks - AnalysisAdvancedNew
MCMC for Conversion-Funnel A/B Testing at a Marketplace
You receive 6 weeks of per-visitor funnel data (visit, sign-up, trial start, trial-to-paid conversion) split by variant and by acquisition channel (organic, paid social, paid se…
- Mcmc
- Bayesian Hierarchical Models
- Ab Testing
Probabilistic Machine Learning - CodeAdvancedNew
Build a Hybrid Recommendation System for an Indie Streaming Catalog
Use the provided 6-month anonymized event log (around 320M play events, 1.4M unique users in the held-out cohort), audio embeddings (256-d), and track metadata. Implement (1) an…
- Recommendation Systems
- Collaborative Filtering
- Content Based Recommendation
Data Mining and Information Retrieval Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisAdvancedNew
Frequent-Itemset Mining on a Grocery Retailer's Basket History
Load 18 months of basket-level transaction data (Parquet, around 92 GB) into a Spark cluster. Run FP-growth at support thresholds tuned per category (food vs household vs fresh)…
- Frequent Itemset Mining
- Fp Growth
- Spark
Data Mining and Information Retrieval - CodeAdvancedNew
Build a Canary Rollout for a Production Recommender
Pick a serving stack (Triton, Seldon Core, KServe, or BentoML). Implement two-model traffic splitting with a configurable percentage (start at 5%). Wire up online metric collect…
- Canary Deployment
- Kubernetes
- Ab Testing
ML Engineering and Production ML
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.